class_docstr: Test to verify PruningSchedule behavior for step parameters.
  
  This is a parameterized test which runs over all PruningSchedule classes
  to ensure they validate parameters such as begin_step, end_step and frequency
  properly and also apply them correctly during execution.
code: def testSparsityValueIsValid(self, schedule_type):
      if schedule_type == "constant_sparsity":
          # pylint: disable=unnecessary-lambda
          self._validate_sparsity(
              lambda s: pruning_schedule.ConstantSparsity(s, 0)
          )
      elif schedule_type == "polynomial_decay":
          self._validate_sparsity(
              lambda s: pruning_schedule.PolynomialDecay(s, 0.8, 0, 10)
          )
          self._validate_sparsity(
              lambda s: pruning_schedule.PolynomialDecay(0.2, s, 0, 10)
          )
  
imports: from absl.testing import parameterized
  import tensorflow
  from tensorflow_model_optimization.python.core.keras import compat
  from tensorflow_model_optimization.python.core.quantization.keras import utils
  from tensorflow_model_optimization.python.core.sparsity.keras import pruning_schedule
label_desc: Use image node_id 7 for calling the PruningScheduleTest obj's underlying member method code with example usage: obj.testSparsityValueIsValid(schedule_type) without return types